mindspore.ops.mean
- mindspore.ops.mean(x, axis=(), keep_dims=False)[source]
Reduces a dimension of a tensor by averaging all elements in the dimension, by default. And also can reduce a dimension of x along the axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.
- Parameters
x (Tensor[Number]) – The input tensor. The dtype of the tensor to be reduced is number. \((N,*)\) where \(*\) means, any number of additional dimensions, its rank should be less than 8.
axis (Union[int, tuple(int), list(int)]) – The dimensions to reduce. Default: (), reduce all dimensions. Only constant value is allowed. Assume the rank of x is r, and the value range is [-r,r).
keep_dims (bool) – If true, keep these reduced dimensions and the length is 1. If false, don’t keep these dimensions. Default: False.
- Returns
Tensor, has the same data type as input tensor.
If axis is (), and keep_dims is False, the output is a 0-D tensor representing the product of all elements in the input tensor.
If axis is int, set as 1, and keep_dims is False, the shape of output is \((x_0, x_2, ..., x_R)\).
If axis is tuple(int), set as (1, 2), and keep_dims is False, the shape of output is \((x_0, x_3, ..., x_R)\).
- Raises
TypeError – If x is not a Tensor.
TypeError – If axis is not one of the following: int, tuple or list.
TypeError – If keep_dims is not a bool.
ValueError – If axis is out of range.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> output = ops.mean(x, 1, keep_dims=True) >>> result = output.shape >>> print(result) (3, 1, 5, 6) >>> # case 1: Reduces a dimension by averaging all elements in the dimension. >>> x = Tensor(np.array([[[2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2]], ... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]], ... [[6, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8], [10, 10, 10, 10, 10, 10]]]), ... mindspore.float32) >>> output = ops.mean(x) >>> print(output) 5.0 >>> print(output.shape) () >>> # case 2: Reduces a dimension along the axis 0 >>> output = ops.mean(x, 0, True) >>> print(output) [[[4. 4. 4. 4. 4. 4.] [5. 5. 5. 5. 5. 5.] [6. 6. 6. 6. 6. 6.]]] >>> # case 3: Reduces a dimension along the axis 1 >>> output = ops.mean(x, 1, True) >>> print(output) [[[2. 2. 2. 2. 2. 2.]] [[5. 5. 5. 5. 5. 5.]] [[8. 8. 8. 8. 8. 8.]]] >>> # case 4: Reduces a dimension along the axis 2 >>> output = ops.mean(x, 2, True) >>> print(output) [[[ 2.] [ 2.] [ 2.]] [[ 4.] [ 5.] [ 6.]] [[ 6.] [ 8.] [10.]]]